Talk by Kelly Clancy, of UC Berkeley. Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley.
Abstract: I'll be talking about a joint effort between the Feldman, Carmena and Costa labs to study abstract task learning by small neuronal assemblies in intact networks. Brain-machine interfaces are a unique tool for studying learning, thanks to the direct mapping between neural activity and reward. We trained mice to operantly control an auditory cursor using spike-related calcium signals recorded with two-photon imaging in motor and somatosensory cortex, allowing us to assess the effects of learning with great spatial detail. Mice rapidly learned to modulate activity in layer 2/3 neurons, evident both across and within sessions. Interestingly, even neurons that exhibited very low or no spontaneous spiking--so-called 'silent' cells that are invisible to electrode-based techniques--could be behaviorally up-modulated for task performance. Learning was accompanied by modifications of firing correlations in spatially localized networks at fine scales.